Overview

Dataset statistics

Number of variables14
Number of observations546
Missing cells546
Missing cells (%)7.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory59.8 KiB
Average record size in memory112.2 B

Variable types

Numeric8
Categorical5
Unsupported1

Warnings

is_failed has constant value "1" Constant
customer_id has a high cardinality: 438 distinct values High cardinality
order_date has a high cardinality: 320 distinct values High cardinality
transmission_id is highly correlated with is_failedHigh correlation
is_failed is highly correlated with transmission_id and 1 other fieldsHigh correlation
payment_id is highly correlated with is_failedHigh correlation
customer_order_rank has 546 (100.0%) missing values Missing
customer_id is uniformly distributed Uniform
df_index has unique values Unique
customer_order_rank is an unsupported type, check if it needs cleaning or further analysis Unsupported
order_hour has 7 (1.3%) zeros Zeros
voucher_amount has 520 (95.2%) zeros Zeros
delivery_fee has 417 (76.4%) zeros Zeros

Reproduction

Analysis started2021-02-25 23:04:57.437821
Analysis finished2021-02-25 23:05:05.667077
Duration8.23 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct546
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean413462.0824
Minimum2699
Maximum786062
Zeros0
Zeros (%)0.0%
Memory size4.4 KiB
2021-02-25T17:05:05.743824image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2699
5-th percentile47132.25
Q1236975
median388468.5
Q3615865.25
95-th percentile757699.75
Maximum786062
Range783363
Interquartile range (IQR)378890.25

Descriptive statistics

Standard deviation227584.7071
Coefficient of variation (CV)0.5504367069
Kurtosis-1.162275483
Mean413462.0824
Median Absolute Deviation (MAD)186137
Skewness-0.02591198913
Sum225750297
Variance5.179479889 × 1010
MonotocityStrictly increasing
2021-02-25T17:05:05.871894image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1464321
 
0.2%
4317561
 
0.2%
2382141
 
0.2%
1429831
 
0.2%
7297361
 
0.2%
3518811
 
0.2%
4317541
 
0.2%
26991
 
0.2%
5865961
 
0.2%
2321581
 
0.2%
Other values (536)536
98.2%
ValueCountFrequency (%)
26991
0.2%
36671
0.2%
41971
0.2%
48351
0.2%
63151
0.2%
ValueCountFrequency (%)
7860621
0.2%
7858181
0.2%
7829621
0.2%
7823931
0.2%
7814611
0.2%

customer_id
Categorical

HIGH CARDINALITY
UNIFORM

Distinct438
Distinct (%)80.2%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
787f67dc55ca
 
10
415e22e514c0
 
5
d956116d863d
 
5
52ecbd9f90cf
 
5
59c241c6aa7c
 
4
Other values (433)
517 

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters6552
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique370 ?
Unique (%)67.8%

Sample

1st row00c9c435cd4a
2nd row010a7ac13c41
3rd row0132d2b7563c
4th row0173219940e8
5th row01e4f035a965
ValueCountFrequency (%)
787f67dc55ca10
 
1.8%
415e22e514c05
 
0.9%
d956116d863d5
 
0.9%
52ecbd9f90cf5
 
0.9%
59c241c6aa7c4
 
0.7%
71d6e47711164
 
0.7%
edcae608c2634
 
0.7%
69ace890f21f4
 
0.7%
3b69f4464d7a4
 
0.7%
525aec76634e4
 
0.7%
Other values (428)497
91.0%
2021-02-25T17:05:06.154539image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
787f67dc55ca10
 
1.8%
415e22e514c05
 
0.9%
d956116d863d5
 
0.9%
52ecbd9f90cf5
 
0.9%
59c241c6aa7c4
 
0.7%
71d6e47711164
 
0.7%
edcae608c2634
 
0.7%
69ace890f21f4
 
0.7%
3b69f4464d7a4
 
0.7%
525aec76634e4
 
0.7%
Other values (428)497
91.0%

Most occurring characters

ValueCountFrequency (%)
7458
 
7.0%
5442
 
6.7%
e442
 
6.7%
1433
 
6.6%
c428
 
6.5%
f428
 
6.5%
6426
 
6.5%
9400
 
6.1%
4400
 
6.1%
b400
 
6.1%
Other values (6)2295
35.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number4058
61.9%
Lowercase Letter2494
38.1%

Most frequent character per category

ValueCountFrequency (%)
7458
11.3%
5442
10.9%
1433
10.7%
6426
10.5%
9400
9.9%
4400
9.9%
8387
9.5%
3375
9.2%
0373
9.2%
2364
9.0%
ValueCountFrequency (%)
e442
17.7%
c428
17.2%
f428
17.2%
b400
16.0%
d399
16.0%
a397
15.9%

Most occurring scripts

ValueCountFrequency (%)
Common4058
61.9%
Latin2494
38.1%

Most frequent character per script

ValueCountFrequency (%)
7458
11.3%
5442
10.9%
1433
10.7%
6426
10.5%
9400
9.9%
4400
9.9%
8387
9.5%
3375
9.2%
0373
9.2%
2364
9.0%
ValueCountFrequency (%)
e442
17.7%
c428
17.2%
f428
17.2%
b400
16.0%
d399
16.0%
a397
15.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII6552
100.0%

Most frequent character per block

ValueCountFrequency (%)
7458
 
7.0%
5442
 
6.7%
e442
 
6.7%
1433
 
6.6%
c428
 
6.5%
f428
 
6.5%
6426
 
6.5%
9400
 
6.1%
4400
 
6.1%
b400
 
6.1%
Other values (6)2295
35.0%

order_date
Categorical

HIGH CARDINALITY

Distinct320
Distinct (%)58.6%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
2016-11-13
 
12
2016-07-17
 
9
2016-07-31
 
8
2016-08-06
 
8
2015-10-10
 
7
Other values (315)
502 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters5460
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique197 ?
Unique (%)36.1%

Sample

1st row2017-01-21
2nd row2016-06-17
3rd row2016-10-12
4th row2015-09-03
5th row2017-02-23
ValueCountFrequency (%)
2016-11-1312
 
2.2%
2016-07-179
 
1.6%
2016-07-318
 
1.5%
2016-08-068
 
1.5%
2015-10-107
 
1.3%
2016-07-116
 
1.1%
2016-07-066
 
1.1%
2016-07-106
 
1.1%
2016-02-165
 
0.9%
2016-03-205
 
0.9%
Other values (310)474
86.8%
2021-02-25T17:05:06.381432image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2016-11-1312
 
2.2%
2016-07-179
 
1.6%
2016-07-318
 
1.5%
2016-08-068
 
1.5%
2015-10-107
 
1.3%
2016-07-116
 
1.1%
2016-07-066
 
1.1%
2016-07-106
 
1.1%
2016-02-165
 
0.9%
2016-03-205
 
0.9%
Other values (310)474
86.8%

Most occurring characters

ValueCountFrequency (%)
01195
21.9%
-1092
20.0%
11058
19.4%
2868
15.9%
6467
 
8.6%
5208
 
3.8%
7192
 
3.5%
3114
 
2.1%
895
 
1.7%
994
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number4368
80.0%
Dash Punctuation1092
 
20.0%

Most frequent character per category

ValueCountFrequency (%)
01195
27.4%
11058
24.2%
2868
19.9%
6467
 
10.7%
5208
 
4.8%
7192
 
4.4%
3114
 
2.6%
895
 
2.2%
994
 
2.2%
477
 
1.8%
ValueCountFrequency (%)
-1092
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01195
21.9%
-1092
20.0%
11058
19.4%
2868
15.9%
6467
 
8.6%
5208
 
3.8%
7192
 
3.5%
3114
 
2.1%
895
 
1.7%
994
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII5460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01195
21.9%
-1092
20.0%
11058
19.4%
2868
15.9%
6467
 
8.6%
5208
 
3.8%
7192
 
3.5%
3114
 
2.1%
895
 
1.7%
994
 
1.7%

order_hour
Real number (ℝ≥0)

ZEROS

Distinct21
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.21611722
Minimum0
Maximum23
Zeros7
Zeros (%)1.3%
Memory size4.4 KiB
2021-02-25T17:05:06.464831image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10
Q115
median18
Q320
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.303721063
Coefficient of variation (CV)0.2499820958
Kurtosis3.641983137
Mean17.21611722
Median Absolute Deviation (MAD)2
Skewness-1.64386451
Sum9400
Variance18.52201499
MonotocityNot monotonic
2021-02-25T17:05:06.566814image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
2081
14.8%
1971
13.0%
1870
12.8%
2149
9.0%
1748
8.8%
2237
6.8%
1432
 
5.9%
1632
 
5.9%
1326
 
4.8%
1221
 
3.8%
Other values (11)79
14.5%
ValueCountFrequency (%)
07
1.3%
11
 
0.2%
21
 
0.2%
38
1.5%
51
 
0.2%
ValueCountFrequency (%)
2317
 
3.1%
2237
6.8%
2149
9.0%
2081
14.8%
1971
13.0%

customer_order_rank
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing546
Missing (%)100.0%
Memory size4.4 KiB

is_failed
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
1
546 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters546
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1
ValueCountFrequency (%)
1546
100.0%
2021-02-25T17:05:06.787922image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-25T17:05:06.851604image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1546
100.0%

Most occurring characters

ValueCountFrequency (%)
1546
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number546
100.0%

Most frequent character per category

ValueCountFrequency (%)
1546
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common546
100.0%

Most frequent character per script

ValueCountFrequency (%)
1546
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII546
100.0%

Most frequent character per block

ValueCountFrequency (%)
1546
100.0%

voucher_amount
Real number (ℝ≥0)

ZEROS

Distinct9
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1096217949
Minimum0
Maximum5.145
Zeros520
Zeros (%)95.2%
Memory size4.4 KiB
2021-02-25T17:05:06.898211image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5.145
Range5.145
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5712117285
Coefficient of variation (CV)5.210749643
Kurtosis47.84982812
Mean0.1096217949
Median Absolute Deviation (MAD)0
Skewness6.511343966
Sum59.8535
Variance0.3262828388
MonotocityNot monotonic
2021-02-25T17:05:06.981583image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0520
95.2%
2.0587
 
1.3%
1.0296
 
1.1%
1.7155
 
0.9%
5.1454
 
0.7%
2.7441
 
0.2%
3.431
 
0.2%
2.57251
 
0.2%
1.3721
 
0.2%
ValueCountFrequency (%)
0520
95.2%
1.0296
 
1.1%
1.3721
 
0.2%
1.7155
 
0.9%
2.0587
 
1.3%
ValueCountFrequency (%)
5.1454
0.7%
3.431
 
0.2%
2.7441
 
0.2%
2.57251
 
0.2%
2.0587
1.3%

delivery_fee
Real number (ℝ≥0)

ZEROS

Distinct19
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2082970147
Minimum0
Maximum2.2185
Zeros417
Zeros (%)76.4%
Memory size4.4 KiB
2021-02-25T17:05:07.068441image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1.4297
Maximum2.2185
Range2.2185
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4338700469
Coefficient of variation (CV)2.082939343
Kurtosis4.347234072
Mean0.2082970147
Median Absolute Deviation (MAD)0
Skewness2.215072646
Sum113.73017
Variance0.1882432176
MonotocityNot monotonic
2021-02-25T17:05:07.157084image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0417
76.4%
0.49339
 
7.1%
0.98623
 
4.2%
1.429716
 
2.9%
0.739514
 
2.6%
0.4683510
 
1.8%
1.4795
 
0.9%
1.23254
 
0.7%
0.24653
 
0.5%
1.72553
 
0.5%
Other values (9)12
 
2.2%
ValueCountFrequency (%)
0417
76.4%
0.24653
 
0.5%
0.34511
 
0.2%
0.4683510
 
1.8%
0.49339
 
7.1%
ValueCountFrequency (%)
2.21851
 
0.2%
2.07062
 
0.4%
1.9722
 
0.4%
1.72553
0.5%
1.4795
0.9%

amount_paid
Real number (ℝ≥0)

Distinct270
Distinct (%)49.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.75323626
Minimum0
Maximum237.5163
Zeros2
Zeros (%)0.4%
Memory size4.4 KiB
2021-02-25T17:05:07.260910image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.24269
Q15.92596
median8.349975
Q311.682
95-th percentile20.709
Maximum237.5163
Range237.5163
Interquartile range (IQR)5.75604

Descriptive statistics

Standard deviation15.47884392
Coefficient of variation (CV)1.439459112
Kurtosis169.1626712
Mean10.75323626
Median Absolute Deviation (MAD)2.747925
Skewness11.95427775
Sum5871.267
Variance239.5946092
MonotocityNot monotonic
2021-02-25T17:05:07.372346image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.3121
 
3.8%
6.37211
 
2.0%
4.2426910
 
1.8%
6.90310
 
1.8%
7.4348
 
1.5%
4.2488
 
1.5%
9.0278
 
1.5%
5.25697
 
1.3%
6.10657
 
1.3%
12.47857
 
1.3%
Other values (260)449
82.2%
ValueCountFrequency (%)
02
0.4%
1.316881
0.2%
1.75231
0.2%
2.6552
0.4%
2.697481
0.2%
ValueCountFrequency (%)
237.51632
0.4%
77.68531
0.2%
62.60491
0.2%
57.3481
0.2%
51.08221
0.2%

restaurant_id
Real number (ℝ≥0)

Distinct432
Distinct (%)79.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean172961373.5
Minimum233498
Maximum332673498
Zeros0
Zeros (%)0.0%
Memory size4.4 KiB
2021-02-25T17:05:07.483543image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum233498
5-th percentile32303498
Q185195998
median183173498
Q3252753498
95-th percentile305593498
Maximum332673498
Range332440000
Interquartile range (IQR)167557500

Descriptive statistics

Standard deviation91254605.2
Coefficient of variation (CV)0.5276010671
Kurtosis-1.262767386
Mean172961373.5
Median Absolute Deviation (MAD)84110000
Skewness-0.1130137734
Sum9.443690991 × 1010
Variance8.32740297 × 1015
MonotocityNot monotonic
2021-02-25T17:05:07.595833image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30559349810
 
1.8%
1545434986
 
1.1%
2869334985
 
0.9%
549234985
 
0.9%
662834985
 
0.9%
2527534984
 
0.7%
2672834984
 
0.7%
2491334984
 
0.7%
817534984
 
0.7%
323034983
 
0.5%
Other values (422)496
90.8%
ValueCountFrequency (%)
2334981
0.2%
4834981
0.2%
5634981
0.2%
22234981
0.2%
29234981
0.2%
ValueCountFrequency (%)
3326734981
0.2%
3244134981
0.2%
3224034981
0.2%
3215734981
0.2%
3210934981
0.2%

city_id
Real number (ℝ≥0)

Distinct202
Distinct (%)37.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46507.63553
Minimum230
Maximum99654
Zeros0
Zeros (%)0.0%
Memory size4.4 KiB
2021-02-25T17:05:07.730999image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum230
5-th percentile10346
Q121710.25
median46779.5
Q365279.75
95-th percentile90633
Maximum99654
Range99424
Interquartile range (IQR)43569.5

Descriptive statistics

Standard deviation26134.6373
Coefficient of variation (CV)0.5619429368
Kurtosis-1.023818627
Mean46507.63553
Median Absolute Deviation (MAD)20386.5
Skewness0.05573643737
Sum25393169
Variance683019266.6
MonotocityNot monotonic
2021-02-25T17:05:07.877469image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1034680
 
14.7%
8056227
 
4.9%
2032622
 
4.0%
4044121
 
3.8%
5089815
 
2.7%
9063312
 
2.2%
4436610
 
1.8%
605379
 
1.6%
472829
 
1.6%
343486
 
1.1%
Other values (192)335
61.4%
ValueCountFrequency (%)
2301
 
0.2%
12986
1.1%
43344
0.7%
47831
 
0.2%
48311
 
0.2%
ValueCountFrequency (%)
996541
0.2%
993152
0.4%
973012
0.4%
962782
0.4%
947001
0.2%

payment_id
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
1491
186 
1779
181 
1619
93 
1811
62 
1523
24 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters2184
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1491
2nd row1491
3rd row1491
4th row1491
5th row1491
ValueCountFrequency (%)
1491186
34.1%
1779181
33.2%
161993
17.0%
181162
 
11.4%
152324
 
4.4%
2021-02-25T17:05:08.153634image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-25T17:05:08.404622image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1491186
34.1%
1779181
33.2%
161993
17.0%
181162
 
11.4%
152324
 
4.4%

Most occurring characters

ValueCountFrequency (%)
1949
43.5%
9460
21.1%
7362
 
16.6%
4186
 
8.5%
693
 
4.3%
862
 
2.8%
524
 
1.1%
224
 
1.1%
324
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2184
100.0%

Most frequent character per category

ValueCountFrequency (%)
1949
43.5%
9460
21.1%
7362
 
16.6%
4186
 
8.5%
693
 
4.3%
862
 
2.8%
524
 
1.1%
224
 
1.1%
324
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common2184
100.0%

Most frequent character per script

ValueCountFrequency (%)
1949
43.5%
9460
21.1%
7362
 
16.6%
4186
 
8.5%
693
 
4.3%
862
 
2.8%
524
 
1.1%
224
 
1.1%
324
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII2184
100.0%

Most frequent character per block

ValueCountFrequency (%)
1949
43.5%
9460
21.1%
7362
 
16.6%
4186
 
8.5%
693
 
4.3%
862
 
2.8%
524
 
1.1%
224
 
1.1%
324
 
1.1%

platform_id
Real number (ℝ≥0)

Distinct8
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29870.09158
Minimum29463
Maximum30391
Zeros0
Zeros (%)0.0%
Memory size4.4 KiB
2021-02-25T17:05:08.500718image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum29463
5-th percentile29463
Q129463
median29815
Q330231
95-th percentile30359
Maximum30391
Range928
Interquartile range (IQR)768

Descriptive statistics

Standard deviation336.6185586
Coefficient of variation (CV)0.01126941837
Kurtosis-1.507933664
Mean29870.09158
Median Absolute Deviation (MAD)352
Skewness0.08296615676
Sum16309070
Variance113312.054
MonotocityNot monotonic
2021-02-25T17:05:08.599988image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
29463172
31.5%
29815157
28.8%
30231148
27.1%
3035942
 
7.7%
2975112
 
2.2%
3039110
 
1.8%
301994
 
0.7%
294951
 
0.2%
ValueCountFrequency (%)
29463172
31.5%
294951
 
0.2%
2975112
 
2.2%
29815157
28.8%
301994
 
0.7%
ValueCountFrequency (%)
3039110
 
1.8%
3035942
 
7.7%
30231148
27.1%
301994
 
0.7%
29815157
28.8%

transmission_id
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
212
420 
4356
59 
4228
 
38
4324
 
26
4996
 
3

Length

Max length4
Median length3
Mean length3.230769231
Min length3

Characters and Unicode

Total characters1764
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row212
2nd row212
3rd row212
4th row212
5th row212
ValueCountFrequency (%)
212420
76.9%
435659
 
10.8%
422838
 
7.0%
432426
 
4.8%
49963
 
0.5%
2021-02-25T17:05:08.826232image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-25T17:05:08.904891image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
212420
76.9%
435659
 
10.8%
422838
 
7.0%
432426
 
4.8%
49963
 
0.5%

Most occurring characters

ValueCountFrequency (%)
2942
53.4%
1420
23.8%
4152
 
8.6%
385
 
4.8%
662
 
3.5%
559
 
3.3%
838
 
2.2%
96
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1764
100.0%

Most frequent character per category

ValueCountFrequency (%)
2942
53.4%
1420
23.8%
4152
 
8.6%
385
 
4.8%
662
 
3.5%
559
 
3.3%
838
 
2.2%
96
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common1764
100.0%

Most frequent character per script

ValueCountFrequency (%)
2942
53.4%
1420
23.8%
4152
 
8.6%
385
 
4.8%
662
 
3.5%
559
 
3.3%
838
 
2.2%
96
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1764
100.0%

Most frequent character per block

ValueCountFrequency (%)
2942
53.4%
1420
23.8%
4152
 
8.6%
385
 
4.8%
662
 
3.5%
559
 
3.3%
838
 
2.2%
96
 
0.3%

Interactions

2021-02-25T17:04:57.937866image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:04:58.060994image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:04:58.204286image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:04:58.339416image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:04:58.467865image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:04:58.608760image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:04:58.744838image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:04:58.880615image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:04:59.004506image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:04:59.120244image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:04:59.231511image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:04:59.343655image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:04:59.481875image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:04:59.595436image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:05:00.024112image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:05:00.156308image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:05:00.269801image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:05:00.383839image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:05:00.506688image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:05:00.631352image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:05:00.750634image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:05:00.870479image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:05:00.996522image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:05:01.092789image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:05:01.190172image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:05:01.286909image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:05:01.390801image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:05:01.490480image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:05:01.593472image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:05:01.722869image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:05:01.833470image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:05:01.950473image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:05:02.064851image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:05:02.185533image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:05:02.301361image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:05:02.401238image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:05:02.542642image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:05:02.668022image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:05:02.782345image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:05:02.888973image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:05:02.999460image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:05:03.108629image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:05:03.232797image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:05:03.367777image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:05:03.484724image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:05:03.604220image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:05:03.705456image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:05:03.813787image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:05:03.938180image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:05:04.057417image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:05:04.186945image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:05:04.295130image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:05:04.406700image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:05:04.644786image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:05:04.755592image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:05:04.881738image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-02-25T17:05:09.014675image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-02-25T17:05:09.240662image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-02-25T17:05:09.468808image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-02-25T17:05:09.690040image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-02-25T17:05:09.857026image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-02-25T17:05:05.123264image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-02-25T17:05:05.408545image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-02-25T17:05:05.539606image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

df_indexcustomer_idorder_dateorder_hourcustomer_order_rankis_failedvoucher_amountdelivery_feeamount_paidrestaurant_idcity_idpayment_idplatform_idtransmission_id
0269900c9c435cd4a2017-01-2118NaN10.00.000016.83273075349840441149129751212
13667010a7ac13c412016-06-1716NaN10.00.00007.965012303349844366149130359212
241970132d2b7563c2016-10-1212NaN10.01.725527.399615454349810346149129463212
348350173219940e82015-09-0318NaN10.00.00008.336722755349810346149129463212
4631501e4f035a9652017-02-2320NaN10.00.00007.80573762349886381149130231212
567540204f88cf9eb2015-10-0916NaN10.00.000015.930088173498462761619298154356
669970219915c55882015-08-0420NaN10.00.00005.787912524349868491619302314324
77467024d7e2832592016-02-2018NaN10.00.00009.8235277163498535711619302314228
8891502d626f7f85b2015-06-1221NaN10.00.000025.912821872349885280149129815212
910057031c273bc06d2015-11-2321NaN10.01.725510.035915454349810346149129463212

Last rows

df_indexcustomer_idorder_dateorder_hourcustomer_order_rankis_failedvoucher_amountdelivery_feeamount_paidrestaurant_idcity_idpayment_idplatform_idtransmission_id
536779268fd8df7703a7f2015-11-1018NaN10.00.0000011.95281190433498443661619298154356
537779610fdb30c3272612016-05-110NaN10.01.479009.239406388349858320149130359212
538779611fdb30c3272612016-05-110NaN10.01.479009.239406388349858320149130359212
539781102fe330d878d702016-12-0512NaN10.00.734576.8392813585349820326149130359212
540781450fe4bd15da8552016-04-0919NaN10.00.0000010.30140611349840441177930231212
541781461fe4bd15da8552016-05-2215NaN10.00.000008.01810611349840441177930231212
542782393fe97f3d7093f2016-09-1118NaN10.00.0000030.1289424561349831813149129463212
543782962fec8c74dc7db2016-09-2716NaN10.00.000009.027007524349869457149129815212
544785818ffbca9c1cc9c2016-11-1814NaN10.00.0000010.673107701349850898149129815212
545786062ffcdbbc627fe2015-09-0322NaN10.00.493009.0270020094349855797149130231212